Machine Learning and Artificial Intelligence in Geosciences: Volume 61
Published on 22. September 2020
Book
Hardback
316 pages
978-0-12-821669-9 (ISBN)
Description
Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more.
More details
Series
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
College/higher education
Graduate students, scientists and engineers of geophysics, physics, acoustics, civil engineering, environmental sciences, geology and planetary sciences
Dimensions
Height: 229 mm
Width: 152 mm
Weight
610 gr
ISBN-13
978-0-12-821669-9 (9780128216699)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
09/2020
Academic Press
€159.00
Available for download
Persons
Ben Moseley works at the Department of Computer Science at the University of Oxford and is currently researching the use of machine learning for seismic simulation and inversion, as well as machine learning for space science. Previously he was a geophysicist in the hydrocarbon industry, with experience in seismic processing, imaging and exploration Lion Krischer works at the Department of Earth Sciences at the ETH Zurich in Switzerland. His works sits at the crossroads where seismology meets computational science, Big Data engineering, and machine learning.
Content
1. Preface
2. 70 years of machine learning in geoscience in review
Jesper Soeren Dramsch
3. Machine learning and fault rupture: A review
Christopher X. Ren, Claudia Hulbert, Paul A. Johnson and Bertrand Rouet-Leduc
4. Machine learning techniques for fractured media
Shriram Srinivasan
5. Seismic signal augmentation to improve generalization of deep neural networks
Weiqiang Zhu , S. Mostafa Mousavi and Gregory C. Beroza
6. Deep generator priors for Bayesian seismic inversion
Zhilong Fang, Hongjian Fang and L. Demanet
7. An introduction to the two-scale homogenization method for seismology
Yann Capdeville, Paul Cupillard and Sneha Singh
2. 70 years of machine learning in geoscience in review
Jesper Soeren Dramsch
3. Machine learning and fault rupture: A review
Christopher X. Ren, Claudia Hulbert, Paul A. Johnson and Bertrand Rouet-Leduc
4. Machine learning techniques for fractured media
Shriram Srinivasan
5. Seismic signal augmentation to improve generalization of deep neural networks
Weiqiang Zhu , S. Mostafa Mousavi and Gregory C. Beroza
6. Deep generator priors for Bayesian seismic inversion
Zhilong Fang, Hongjian Fang and L. Demanet
7. An introduction to the two-scale homogenization method for seismology
Yann Capdeville, Paul Cupillard and Sneha Singh